Preeclampsia Susceptibility Assessment Based on Deep Learning Modeling and Single Nucleotide Polymorphism Analysis.

The early diagnosis of preeclampsia, a key outlook in improving pregnancy outcomes, still remains elusive. The present study aimed to examine the interleukin-13 and interleukin-4 pathway potential in the early detection of preeclampsia as well as the relationship between interleukin-13 rs2069740(T/A) and rs34255686(C/A) polymorphisms and preeclampsia risk to present a combined model. This study utilized raw data from the GSE149440 microarray dataset, and an expression matrix was constructed using the RMA method and affy package. The genes related to the interleukin-13 and interleukin-4 pathway were extracted from the GSEA, and their expression levels were applied to design multilayer perceptron and PPI graph convolutional neural network models. Moreover, genotyping for the rs2069740(T/A) and rs34255686(C/A) polymorphisms of the interleukin-13 gene were tested using the amplification refractory mutation system PCR method. The outcomes revealed that the expression levels of interleukin-4 and interleukin-13 pathway genes could significantly differentiate early preeclampsia from normal pregnancy. Moreover, the present study's data suggested significant differences in the genotype distribution, the allelic frequencies and some of the risk markers of the study, in the position of rs34255686 and rs2069740 polymorphisms between the case and control groups. A combined test of two single nucleotide polymorphisms and an expression-based deep learning model could be designed for future preeclampsia diagnostic purposes.

iTCep: a deep learning framework for identification of T cell epitopes by harnessing fusion features.

Neoantigens recognized by cytotoxic T cells are effective targets for tumor-specific immune responses for personalized cancer immunotherapy. Quite a few neoantigen identification pipelines and computational strategies have been developed to improve the accuracy of the peptide selection process. However, these methods mainly consider the neoantigen end and ignore the interaction between peptide-TCR and the preference of each residue in TCRs, resulting in the filtered peptides often fail to truly elicit an immune response. Here, we propose a novel encoding approach for peptide-TCR representation. Subsequently, a deep learning framework, namely iTCep, was developed to predict the interactions between peptides and TCRs using fusion features derived from a feature-level fusion strategy. The iTCep achieved high predictive performance with AUC up to 0.96 on the testing dataset and above 0.86 on independent datasets, presenting better prediction performance compared with other predictors. Our results provided strong evidence that model iTCep can be a reliable and robust method for predicting TCR binding specificities of given antigen peptides. One can access the iTCep through a user-friendly web server at http://biostatistics.online/iTCep/, which supports prediction modes of peptide-TCR pairs and peptide-only. A stand-alone software program for T cell epitope prediction is also available for convenient installing at https://github.com/kbvstmd/iTCep/.

Automated Detection and Scoring of Tumor-Infiltrating Lymphocytes in Breast Cancer Histopathology Slides.

Detection of tumor-infiltrating lymphocytes (TILs) in cancer images has gained significant importance as these lymphocytes can be used as a biomarker in cancer detection and treatment procedures. Our goal was to develop and apply a TILs detection tool that utilizes deep learning models, following two sequential steps. First, based on the guidelines from the International Immuno-Oncology Biomarker Working Group (IIOBWG) on Breast Cancer, we labeled 63 large pathology imaging slides and annotated the TILs in the stroma area to create the dataset required for model development. In the second step, various machine learning models were employed and trained to detect the stroma where U-Net deep learning structure was able to achieve 98% accuracy. After detecting the stroma area, a Mask R-CNN model was employed for the TILs detection task. The R-CNN model detected the TILs in various images and was used as the backbone analysis network for the GUI development of the TILs detection tool. This is the first study to combine two deep learning models for TILs detection at the cellular level in breast tumor histopathology slides. Our novel approach can be applied to scoring TILs in large cancer slides. Statistical analysis showed that the output of the implemented approach had 95% concordance with the scores assigned by the pathologists, with a p-value of 0.045 (n = 63). This demonstrated that the results from the developed software were statistically meaningful and highly accurate. The implemented approach in analyzing whole tumor histology slides and the newly developed TILs detection tool can be used for research purposes in biomedical and pathology applications and it can provide researchers and clinicians with the TIL score for various input images. Future research using additional breast cancer slides from various sources for further training and validation of the developed models is necessary for more inclusive, rigorous, and robust clinical applications.